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<li class="navelem"><a class="el" href="dir_6b3ae6988449b0834e9596fad5d75199.html">gpu</a></li><li class="navelem"><a class="el" href="dir_49d1182a3b8dfb62757c53ae905481ad.html">impl</a></li>  </ul>
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<div class="title">L2Select.cu</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/**</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Copyright (c) 2015-present, Facebook, Inc.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> * All rights reserved.</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> * This source code is licensed under the BSD+Patents license found in the</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> * LICENSE file in the root directory of this source tree.</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;</div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment">// Copyright 2004-present Facebook. All Rights Reserved.</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;</div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#include &quot;L2Select.cuh&quot;</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="preprocessor">#include &quot;../../FaissAssert.h&quot;</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;</div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="preprocessor">#include &quot;../utils/DeviceUtils.h&quot;</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="preprocessor">#include &quot;../utils/MathOperators.cuh&quot;</span></div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="preprocessor">#include &quot;../utils/Pair.cuh&quot;</span></div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="preprocessor">#include &quot;../utils/Reductions.cuh&quot;</span></div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="preprocessor">#include &quot;../utils/Select.cuh&quot;</span></div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="preprocessor">#include &quot;../utils/Tensor.cuh&quot;</span></div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="preprocessor">#include &quot;../utils/StaticUtils.h&quot;</span></div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;</div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;</div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="comment">// L2 + select kernel for k == 1, implements re-use of ||c||^2</span></div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keywordtype">int</span> kRowsPerBlock, <span class="keywordtype">int</span> kBlockSize&gt;</div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;__global__ <span class="keywordtype">void</span> l2SelectMin1(Tensor&lt;T, 2, true&gt; productDistances,</div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;                             Tensor&lt;T, 1, true&gt; centroidDistances,</div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;                             Tensor&lt;T, 2, true&gt; outDistances,</div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;                             Tensor&lt;int, 2, true&gt; outIndices) {</div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;  <span class="comment">// Each block handles kRowsPerBlock rows of the distances (results)</span></div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;  Pair&lt;T, int&gt; threadMin[kRowsPerBlock];</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160;  __shared__ Pair&lt;T, int&gt; blockMin[kRowsPerBlock * (kBlockSize / kWarpSize)];</div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;  T distance[kRowsPerBlock];</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="preprocessor"></span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; kRowsPerBlock; ++i) {</div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;    threadMin[i].k = Limits&lt;T&gt;::getMax();</div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;    threadMin[i].v = -1;</div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;  }</div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;</div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;  <span class="comment">// blockIdx.x: which chunk of rows we are responsible for updating</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;  <span class="keywordtype">int</span> rowStart = blockIdx.x * kRowsPerBlock;</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  <span class="comment">// FIXME: if we have exact multiples, don&#39;t need this</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;  <span class="keywordtype">bool</span> endRow = (blockIdx.x == gridDim.x - 1);</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;  <span class="keywordflow">if</span> (endRow) {</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    <span class="keywordflow">if</span> (productDistances.getSize(0) % kRowsPerBlock == 0) {</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;      endRow = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    }</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  }</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  <span class="keywordflow">if</span> (endRow) {</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = rowStart; row &lt; productDistances.getSize(0); ++row) {</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = threadIdx.x; col &lt; productDistances.getSize(1);</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;           col += blockDim.x) {</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;        distance[0] = Math&lt;T&gt;::add(centroidDistances[col],</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;                                   productDistances[row][col]);</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;        <span class="keywordflow">if</span> (Math&lt;T&gt;::lt(distance[0], threadMin[0].k)) {</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;          threadMin[0].k = distance[0];</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;          threadMin[0].v = col;</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;        }</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;      }</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;      <span class="comment">// Reduce within the block</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;      threadMin[0] =</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;        blockReduceAll&lt;Pair&lt;T, int&gt;, Min&lt;Pair&lt;T, int&gt; &gt;, <span class="keyword">false</span>, <span class="keyword">false</span>&gt;(</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;        threadMin[0], Min&lt;Pair&lt;T, int&gt; &gt;(), blockMin);</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;      <span class="keywordflow">if</span> (threadIdx.x == 0) {</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;        outDistances[row][0] = threadMin[0].k;</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;        outIndices[row][0] = threadMin[0].v;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;      }</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;      <span class="comment">// so we can use the shared memory again</span></div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;      __syncthreads();</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      threadMin[0].k = Limits&lt;T&gt;::getMax();</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;      threadMin[0].v = -1;</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    }</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> col = threadIdx.x; col &lt; productDistances.getSize(1);</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;         col += blockDim.x) {</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;      T centroidDistance = centroidDistances[col];</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;<span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;<span class="preprocessor"></span>      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row &lt; kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;        distance[row] = productDistances[rowStart + row][col];</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;      }</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;<span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;<span class="preprocessor"></span>      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row &lt; kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;        distance[row] = Math&lt;T&gt;::add(distance[row], centroidDistance);</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;      }</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;<span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;<span class="preprocessor"></span>      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row &lt; kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        <span class="keywordflow">if</span> (Math&lt;T&gt;::lt(distance[row], threadMin[row].k)) {</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;          threadMin[row].k = distance[row];</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;          threadMin[row].v = col;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        }</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      }</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    }</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    <span class="comment">// Reduce within the block</span></div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    blockReduceAll&lt;kRowsPerBlock, Pair&lt;T, int&gt;, Min&lt;Pair&lt;T, int&gt; &gt;, <span class="keyword">false</span>, <span class="keyword">false</span>&gt;(</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;      threadMin, Min&lt;Pair&lt;T, int&gt; &gt;(), blockMin);</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keywordflow">if</span> (threadIdx.x == 0) {</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;<span class="preprocessor">#pragma unroll</span></div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;<span class="preprocessor"></span>      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> row = 0; row &lt; kRowsPerBlock; ++row) {</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;        outDistances[rowStart + row][0] = threadMin[row].k;</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;        outIndices[rowStart + row][0] = threadMin[row].v;</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;      }</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;    }</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;  }</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;}</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;<span class="comment">// L2 + select kernel for k &gt; 1, no re-use of ||c||^2</span></div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T, <span class="keywordtype">int</span> NumWarpQ, <span class="keywordtype">int</span> NumThreadQ, <span class="keywordtype">int</span> ThreadsPerBlock&gt;</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;__global__ <span class="keywordtype">void</span> l2SelectMinK(Tensor&lt;T, 2, true&gt; productDistances,</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;                             Tensor&lt;T, 1, true&gt; centroidDistances,</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;                             Tensor&lt;T, 2, true&gt; outDistances,</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;                             Tensor&lt;int, 2, true&gt; outIndices,</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;                             <span class="keywordtype">int</span> k, T initK) {</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  <span class="comment">// Each block handles a single row of the distances (results)</span></div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;  constexpr <span class="keywordtype">int</span> kNumWarps = ThreadsPerBlock / kWarpSize;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;  __shared__ T smemK[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;  __shared__ <span class="keywordtype">int</span> smemV[kNumWarps * NumWarpQ];</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;  BlockSelect&lt;T, int, false, Comparator&lt;T&gt;,</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;              NumWarpQ, NumThreadQ, ThreadsPerBlock&gt;</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;    heap(initK, -1, smemK, smemV, k);</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  <span class="keywordtype">int</span> row = blockIdx.x;</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  <span class="comment">// Whole warps must participate in the selection</span></div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;  <span class="keywordtype">int</span> limit = utils::roundDown(productDistances.getSize(1), kWarpSize);</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;  <span class="keywordtype">int</span> i = threadIdx.x;</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;  <span class="keywordflow">for</span> (; i &lt; limit; i += blockDim.x) {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    T v = Math&lt;T&gt;::add(centroidDistances[i],</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;                       productDistances[row][i]);</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    heap.add(v, i);</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;  }</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;  <span class="keywordflow">if</span> (i &lt; productDistances.getSize(1)) {</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;    T v = Math&lt;T&gt;::add(centroidDistances[i],</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;                       productDistances[row][i]);</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;    heap.addThreadQ(v, i);</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;  }</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;  heap.reduce();</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = threadIdx.x; i &lt; k; i += blockDim.x) {</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;    outDistances[row][i] = smemK[i];</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;    outIndices[row][i] = smemV[i];</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;  }</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;}</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;<span class="comment">// FIXME: no TVec specialization</span></div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;<span class="keywordtype">void</span> runL2SelectMin(Tensor&lt;T, 2, true&gt;&amp; productDistances,</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;                    Tensor&lt;T, 1, true&gt;&amp; centroidDistances,</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;                    Tensor&lt;T, 2, true&gt;&amp; outDistances,</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;                    Tensor&lt;int, 2, true&gt;&amp; outIndices,</div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;                    <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;                    cudaStream_t stream) {</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;  FAISS_ASSERT(productDistances.getSize(0) == outDistances.getSize(0));</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;  FAISS_ASSERT(productDistances.getSize(0) == outIndices.getSize(0));</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  FAISS_ASSERT(centroidDistances.getSize(0) == productDistances.getSize(1));</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;  FAISS_ASSERT(outDistances.getSize(1) == k);</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  FAISS_ASSERT(outIndices.getSize(1) == k);</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;  FAISS_ASSERT(k &lt;= 1024);</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;  <span class="keywordflow">if</span> (k == 1) {</div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;    constexpr <span class="keywordtype">int</span> kThreadsPerBlock = 256;</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;    constexpr <span class="keywordtype">int</span> kRowsPerBlock = 8;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <span class="keyword">auto</span> block = dim3(kThreadsPerBlock);</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keyword">auto</span> grid = dim3(utils::divUp(outDistances.getSize(0), kRowsPerBlock));</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;    l2SelectMin1&lt;T, kRowsPerBlock, kThreadsPerBlock&gt;</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;      &lt;&lt;&lt;grid, block, 0, stream&gt;&gt;&gt;(productDistances, centroidDistances,</div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;                                   outDistances, outIndices);</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;  } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;    constexpr <span class="keywordtype">int</span> kThreadsPerBlock = 128;</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;    <span class="keyword">auto</span> block = dim3(kThreadsPerBlock);</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;    <span class="keyword">auto</span> grid = dim3(outDistances.getSize(0));</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;<span class="preprocessor">#define RUN_L2_SELECT(NUM_WARP_Q, NUM_THREAD_Q)                         \</span></div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;<span class="preprocessor">    do {                                                                \</span></div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;<span class="preprocessor">      l2SelectMinK&lt;T, NUM_WARP_Q, NUM_THREAD_Q, kThreadsPerBlock&gt;       \</span></div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;<span class="preprocessor">        &lt;&lt;&lt;grid, block, 0, stream&gt;&gt;&gt;(productDistances, centroidDistances, \</span></div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;<span class="preprocessor">                                     outDistances, outIndices,          \</span></div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;<span class="preprocessor">                                     k, Limits&lt;T&gt;::getMax());           \</span></div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;<span class="preprocessor">    } while (0)</span></div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;    <span class="keywordflow">if</span> (k &lt;= 32) {</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;      RUN_L2_SELECT(32, 2);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k &lt;= 64) {</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;      RUN_L2_SELECT(64, 3);</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k &lt;= 128) {</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;      RUN_L2_SELECT(128, 3);</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k &lt;= 256) {</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;      RUN_L2_SELECT(256, 4);</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k &lt;= 512) {</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;      RUN_L2_SELECT(512, 8);</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    } <span class="keywordflow">else</span> <span class="keywordflow">if</span> (k &lt;= 1024) {</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;      RUN_L2_SELECT(1024, 8);</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      FAISS_ASSERT(<span class="keyword">false</span>);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;    }</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;  CUDA_TEST_ERROR();</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;}</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;<span class="keywordtype">void</span> runL2SelectMin(Tensor&lt;float, 2, true&gt;&amp; productDistances,</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;                    Tensor&lt;float, 1, true&gt;&amp; centroidDistances,</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;                    Tensor&lt;float, 2, true&gt;&amp; outDistances,</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;                    Tensor&lt;int, 2, true&gt;&amp; outIndices,</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;                    <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;                    cudaStream_t stream) {</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;  runL2SelectMin&lt;float&gt;(productDistances,</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;                        centroidDistances,</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;                        outDistances,</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;                        outIndices,</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;                        k,</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;                        stream);</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;}</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;<span class="preprocessor">#ifdef FAISS_USE_FLOAT16</span></div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;<span class="preprocessor"></span><span class="keywordtype">void</span> runL2SelectMin(Tensor&lt;half, 2, true&gt;&amp; productDistances,</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;                    Tensor&lt;half, 1, true&gt;&amp; centroidDistances,</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;                    Tensor&lt;half, 2, true&gt;&amp; outDistances,</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;                    Tensor&lt;int, 2, true&gt;&amp; outIndices,</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;                    <span class="keywordtype">int</span> k,</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;                    cudaStream_t stream) {</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;  runL2SelectMin&lt;half&gt;(productDistances,</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;                       centroidDistances,</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;                       outDistances,</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;                       outIndices,</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;                       k,</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;                       stream);</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;}</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;<span class="preprocessor"></span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;} } <span class="comment">// namespace</span></div>
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